Editor's Note: Benjamin Alamar, 39 (University of Minnesota, Class of 1996), is an author, researcher and consultant in sports analytics. His upcoming book, "Sports Analytics: A Guide for Coaches, Managers, and Other Decision Makers," is based on his experiences working with NFL and NBA teams, as well as multiple media outlets and companies. He teaches at Menlo College and the University of San Francisco and lives in San Francisco with his wife, Amy (Minnesota, Class of 1997), and their three children.

When I was an economics major at the University of Minnesota in the mid-'90s, there were exactly zero statisticians employed by NBA and NFL teams and the term "Moneyball" was not even a glimmer in author Michael Lewis' eye. The field of sports analytics did not really exist outside of baseball, where it was slowly building momentum, so I had no designs on a career in professional sports. I was, however, a huge sports fan who took every opportunity to liven up my academics with examples in sports. I wrote papers on the inefficiency of the NFL draft for a finance class, and on the power of agents in collective bargaining for labor economics.

I had no idea at the time, but these early papers, which were my first attempts at applying economic theory and data analysis to sports, would become the building blocks for my career.

From the U, I followed one of my professors, Stephen LeRoy, to the University of California at Santa Barbara to pursue a doctorate in economics.

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At UCSB, I dabbled in sports again, but had a focus in public policy analysis. When a startup fantasy sports company hired me in 2004, I discovered the potential for a career in sports analytics. While consulting with the startup, I got my first taste of what was possible through a project for the Portland Trail Blazers. The Blazers had asked us to develop a statistical model to help select players in the 2005 NBA draft. My career in the NBA grew directly from that project and set the stage for me to spend five seasons with the Oklahoma City Thunder and one with my current team, the Cleveland Cavaliers.

While working with teams, I also have been a researcher and professor. My research has focused mostly on the NFL, in which I found, for example, that running the ball usually has a higher likelihood of "failure" than passing.

Simply put, sports analytics is using data and technology to help make better decisions -- gaining an edge over the competition. As data and technology evolve and grow, however, the application of analytics becomes more complex. While decision makers in sports possess deep and valuable knowledge about their sports, they rarely possess deep knowledge on how to apply the tools of analytics in the most impactful way for their organizations.

Adding and growing analytics can be expensive in both time and money, and no team wants to waste either. This leads to one of the impediments to teams using analytics: Decision makers see there is value but are unsure of the best way to maximize their investment. My upcoming book attempts to provide decision makers who have no background in analytics with a deeper understanding of what analytics can do for the organization and how to best manage and invest in analytics to gain a competitive advantage.

In the eight years since that first project with the Blazers, the world of sports analytics has changed dramatically. In 2005, Dean Oliver was the only statistical analyst working in the NBA; now, almost all teams have at least one person utilizing data to help inform the decision makers. In 2005, cutting-edge work manipulating box-score data helped us gain a clearer picture of how teams won and lost and how some players contributed. Now, cutting-edge work in the NBA involves processing data that tracks the movement of the ball, and every player on the court, 25 times a second. The NFL and Major League Baseball have changed dramatically, as well. Virtually every MLB team has a group of analysts, and more and more NFL teams are adding analytics.

Sports analytics has evolved rapidly as a field because technology has allowed for a wider distribution of data and more efficient means of processing the data -- and because coaches and general managers have seen that good analytics can bring a real competitive advantage.

For example, the financially strapped Oakland A's famously applied the tools of analytics in support of their strategy ("Moneyball") of finding undervalued players. They realized that walks could be as valuable as hits, that an inexpensive middle reliever who could strand runners was more valuable than a high-priced closer who started and finished one inning.

Identifying undervalued talent, however, is not the only purpose of analytics. Every team and organization has its own strategy and constraints, and one of the great challenges is to align analytics with those.

Player development is an area ripe for analytics, which can quickly and accurately give players feedback on how they are practicing and playing. For example, a vital skill for a batter in baseball is to recognize the type of pitch coming at him and whether it is a ball or a strike. Combining pitch-by-pitch data with video makes it possible to create a virtual batting experience for a batter to "practice" against any MLB pitcher. The batter would see video of a pitcher getting ready to deliver, then be able to identify both the pitch type and whether it was a ball or a strike. The virtual environment then can give the batter immediate feedback on the accuracy and speed, allowing him to practice and adjust -- developing this vital skill.

From the data and technology side, a budding analyst researching how historical trends in NFL passing efficiency related to playoff success used to open the football encyclopedia and manually enter the data, team by team, season by season. I know because that is exactly what I had to do. Now the same data set can be assembled in a few moments from sites such as pro-football-reference.com. Increasing access to data has made it easier for interested analysts to get involved and see if they can produce new insights.

For teams, analytics provides the coaches and general managers with insight and information that they did not have and that the competition still does not have. My econ professors at the U regularly beat into my brain that better information leads to a competitive advantage, and it turns out that is as true in sports as in economics.

Good analytics also saves coaches and general managers time, with the technology to process and access data more efficiently. The extraordinarily busy coaches and general managers can spend less time gathering information and more time analyzing it. For example, traditionally, if a general manager were interested in salary, performance and medical information on a player, that involved three phone calls to three different staffs and an indeterminate wait time to get the information. Good analytic systems, however, allow the general manager instant access to all of the information, so he can spend his time sorting through it and not gathering it.

As sports analytics becomes more prevalent in the professional leagues, colleges and high schools are starting to pick up the tools. Whether that is through better analysis of the data they have (such as on-base percentage in baseball instead of batting average) or through the creation of new data sets from video-tracking services (such as Krossover Intelligence), teams at all levels have increasing access to more advanced analytic tools.

The growth of analytics has been remarkable since my days at the U, and it is really just getting started.